Systems and methods for modeling item damage severity

ABSTRACT

Systems and methods for explaining year over year changes in claim variables are provided. A computing system is configured to receive claim datasets corresponding to one or more time periods, and parse a plurality of claim variables from each claim dataset. The computing system is also configured to cause one or more machine learning models to parse a plurality of explainer values from each of the claim datasets, determine an average explainer value for each of the plurality of explainer values, and determine percent impact values that each correspond to a particular claim variable. The computing system is also configured to generate and render a user interface having one or more selectable features that each represent one of the percent impact values. The computing system is also configured to filter and sort the one or more selectable features based on the percent impact values.

TECHNICAL FIELD

The present disclosure relates generally to systems and methods formodeling item damage severity for an insured item. Insured items mayinclude a variety of tangible items, such as vehicles, boats, houses,household items, etc. As utilized herein, the terms “severity”, “itemdamage severity”, and other similar terms may refer to a quantitative orqualitative description of damage to an item, a quantitative value ofdamage relative to a baseline value, a dollar amount needed torepair/replace damaged items, a qualitative descriptor, and/or otherdescriptors related to the magnitude of item damage. The terms “item”and “insured item” are used interchangeably.

BACKGROUND

Insurance claims are provided to insurance providers to receiveinsurance benefits, such as payouts, when an insured item is lost ordamaged. Insurance providers may analyze insurance claims in order todetermine item damage severity and the associated expected payout amountin a given time period. However, analyzing large amounts of insurancedata, such as claims where each claim has multiple variables impactingthe item damage severity, may be time consuming and inaccurate.

SUMMARY

At least one embodiment relates to a provider computing system. Theprovider computing system includes a communication interface structuredto communicatively couple the provider computing system to a network.The provider computing system also includes a claims database storingclaims information for a plurality of claims. The claims informationincludes a plurality of claim variables. The provider computing systemalso includes an item damage severity database storing severityinformation. The provider computing system also includes an item damageseverity modeling circuit storing computer-executable instructionsembodying one or more machine learning models. The provider computingsystem also includes at least one processor and memory storinginstructions that, when executed by the at least one processor, causethe at least one processor to: receive a first claim datasetcorresponding to a first time period; parse a first plurality ofvariables from the first claim dataset; receive a second claim datasetcorresponding to a second time period before the first time period;parse a second plurality of variables from the second claim dataset;cause, by the item damage severity modeling circuit, the one or moremachine learning models to parse a first plurality of explainer valuesfrom the first claim dataset and a second plurality of explainer valuesfrom the second claim dataset; determine a first plurality of averageexplainer values for each of the first plurality of explainer values anda second plurality of average explainer values for each of the secondplurality of explainer values; determine percent impact values, whereineach of the percent impact values correspond to a first claim variableof the first plurality of variables and a second claim variable of thesecond plurality of variables, and wherein the first claim variablecorresponds to the second claim variable; generate and render, via adisplay of a computing device, a damage severity user interfacecomprising one or more selectable features, the one or more selectablefeatures each representing one of the percent impact values; and filterand sort the one or more selectable features based on the percent impactvalues and a predetermined impact threshold such that the one or moreselectable features representing the percent impact values that areabove the predetermined impact threshold are ordered from left to rightin descending order.

Another embodiment relates to a method. The method includescommunicatively coupling, by a communication interface, a providercomputing system to a network. The method also includes storing, by aclaims database, claims information for a plurality of claims. Theclaims information includes a plurality of claim variables. The methodalso includes storing, by an item damage severity database, severityinformation. The method also includes storing, by an item damageseverity modeling circuit, computer-executable instructions embodyingone or more machine learning models. The method also includes receivinga first claim dataset corresponding to a first time period. The methodalso includes parsing a first plurality of variables from the firstclaim dataset. The method also includes receiving a second claim datasetcorresponding to a second time period before the first time period. Themethod also includes parsing a second plurality of variables from thesecond claim dataset. The method also includes causing, by an itemdamage severity modeling circuit of the provider computing system, theone or more machine learning models to parse a first plurality ofexplainer values from the first claim dataset and a second plurality ofexplainer values from the second claim dataset. The method also includesdetermining a first plurality of average explainer values for each ofthe first plurality of explainer values and a second plurality ofaverage explainer values for each of the second plurality of explainervalues. The method also includes determining percent impact values,wherein each of the percent impact values correspond to a first claimvariable of the first plurality of variables and a second claim variableof the second plurality of variables, and wherein the first claimvariable corresponds to the second claim variable. The method alsoincludes generating and rendering, via a display of a computing device,a damage severity user interface comprising one or more selectablefeatures, the one or more selectable features each representing one ofthe percent impact values. The method also includes filtering andsorting the one or more selectable features based on the percent impactvalues and a predetermined impact threshold such that the one or moreselectable features representing the percent impact values that areabove the predetermined impact threshold are ordered from left to rightin descending order.

Another embodiment relates to non-transitory computer readable mediahaving computer executable instructions embodied therein that, whenexecuted by at least one processor of a computing system, cause thecomputing system to perform operations for generating multi-variableseverity values. The operations include communicatively couple, by acommunication interface, to a network. The operations also includestore, by a claims database, claims information for a plurality ofclaims. The claims information includes a plurality of claim variables.The operations also include store, by an item damage severity database,severity information. The operations also include store, by an itemdamage severity modeling circuit, computer-executable instructionsembodying one or more machine learning models. The operations alsoinclude receive a first claim dataset corresponding to a first timeperiod. The operations also include parse a first plurality of variablesfrom the first claim dataset. The operations also include receive asecond claim dataset corresponding to a second time period before thefirst time period. The operations also include parse a second pluralityof variables from the second claim dataset. The operations also includecause the one or more machine learning models to parse a first pluralityof explainer values from the first claim dataset and a second pluralityof explainer values from the second claim dataset. The operations alsoinclude determine a first plurality of average explainer values for eachof the first plurality of explainer values and a second plurality ofaverage explainer values for each of the second plurality of explainervalues. The operations also include determine percent impact values.Each of the percent impact values correspond to a first claim variableof the first plurality of variables and a second claim variable of thesecond plurality of variables, and wherein the first claim variablecorresponds to the second claim variable. The operations also includegenerate and render, via a display of a computing device, a damageseverity user interface comprising one or more selectable features, theone or more selectable features each representing one of the percentimpact values. The operations also include filter and sort the one ormore selectable features based on the percent impact values and apredetermined impact threshold such that the one or more selectablefeatures representing the percent impact values that are above thepredetermined impact threshold are ordered from left to right indescending order.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts discussed in greater detail below (provided suchconcepts are not mutually inconsistent) are contemplated as being partof the subject matter disclosed herein. In particular, all combinationsof claimed subject matter appearing at the end of this disclosure arecontemplated as being part of the subject matter disclosed herein.

The foregoing and other features of the present disclosure will becomemore fully apparent from the following description and appended claims,taken in conjunction with the accompanying drawings. Understanding thatthese drawings depict only several implementations in accordance withthe disclosure and are therefore, not to be considered limiting of itsscope, the disclosure will be described with additional specificity anddetail through use of the accompanying drawings.

These and other advantages and features of the systems and methodsdescribed herein, together with the organization and manner of operationthereof, will become apparent from the following detailed descriptionwhen taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are block diagrams of a computing system, according tovarious example embodiments.

FIG. 2 is a flow diagram including computer-based operations fortraining a machine learning model.

FIG. 3 is a flow diagram including computer-based operations fordetermining a multi-variable percent change in item damage severity.

FIG. 4A is an illustration showing various aspects of a user interface,according to an example embodiment.

FIGS. 4B-4D are illustrations showing various aspects of the userinterface of FIG. 4A.

FIG. 5 is a component diagram of an example computing system suitablefor use in the various embodiments described herein.

DETAILED DESCRIPTION

Referring generally to the figures, disclosed are systems, methods andnon-transitory computer-readable media for a provider computing systemfor determining item damage severity.

In conventional claims processing systems, item damage severity isdetermined retroactively—that is, when all factors that impact the itemdamage severity are fully known. Severity is also conventionallyanalyzed using a single-variable approach, where the impact of eachvariable is determined separately from other variables. Conventionalseverity investigations therefore result in large amounts of data foreach individual variable, and, in some instances, may be inaccurate dueto the limited single variable scope.

Accordingly, the systems, methods, and computer-executable mediadescribed herein provide an improved computing system for determiningseverity based on a multi-variable approach. The improved computingsystems advantageously predict severity based on claims data such thatseverity for claims from a first time period can be predicted, ratherthan determined retroactively. Additionally, the systems, methods, andcomputer-executable media described herein provide an improved userinterface that advantageously provides severity data. The improved userinterface may reduce the amount of data transmissions necessary for auser to understand a determined severity, for example, by reducing thenumber of graphics (e.g., graphs, tables, text, etc.) needed to visuallyrepresent the determined severity. Further, the improved user interfaceadvantageously filters and sorts the severity data such that relativelymore relevant severity data is presented before and/or instead ofrelatively less relevant severity data. For example, relatively lessrelevant (e.g., lower magnitude) severity values may be automaticallygrouped into an “other” category and displayed as a single graphicalfeature. Thus the improved user interface provides at least one specificimprovement over prior systems, for example, by reducing the number ofgraphical elements needed to understandably convey severity data.Additionally, the systems, methods, and computer-executable mediadescribed herein embody a self-correcting predictive system that isperiodically re-trained using current data such that the accuracy ofpredictions for item damage severity is improved over time.

In an example illustrative scenario, a provider (e.g., an insuranceprovider) receives damage data for an insured item, such as a vehicle,boat, household appliance, home, etc. In some embodiments, the damagedata is included, at least in part, in one or more insurance claims. Aclaim may include first notice of loss (FNOL) and claim data for aninsured item or for an item associated with an insured item. The claimdata includes one or more claim variables. In some embodiments, aprovider computing system may receive some or the entirety of damagedata from a telematics device and/or another computing device associatedwith a customer of the provider, a provider employee, or a provideragent. In some embodiments, the damage data may be received from aclaims processing device and/or computing system. The provider computingsystem may include one or more machine learning models embodied in oneor more circuits for analyzing the claims. The provider computing systemmay parse or otherwise extract the variables that impact item damageseverity from the damage data. The provider computing system maydetermine a severity impact percentage and/or other related information(trending data, absolute values, averages, periodic change, predictedvalue(s) for subsequent time periods, etc.) for each of the claimvariables, and provide a detailed user interface to display these valuesin a user-interactive format.

The one or more machine learning models may utilize one or more models,frameworks, or other software, programming languages, libraries, etc. Inan example embodiment, the one or more machine learning models mayutilize a machine learning explanatory model, such as Shapley AdditiveExplanations (SHAP) to further analyze one or more variables of the oneor more machine learning models. Accordingly, the one or more machinelearning models may include a machine learning explanatory model, suchas SHAP and/or other suitable explanatory model. In an example operatingscenario, the one or more machine learning models are trained usingclaim data and real item damage severity data associated with the claimdata. The one or more trained machine learning models receive claim dataand output and/or determine an expected severity based on the claimdata. The claim data includes one or more claim variables. The one ormore machine learning models may utilize SHAP to “explain” (e.g., outputand/or determine a quantitative value for) each of the one or more claimvariables. Accordingly, the one or more machine learning models mayoutput and/or determine, using SHAP, an item damage severity for eachclaim variable of each claim. In other example embodiments, the one ormore machine learning models may utilize Pandas, XGBoost, and/or othersuitable executable code libraries.

Before turning to the figures, which illustrate certain exampleembodiments in detail, it should be understood that the presentdisclosure is not limited to the details or methodology set forth in thedescription or illustrated in the figures. It should also be understoodthat the terminology used herein is for the purpose of description onlyand should not be regarded as limiting.

FIGS. 1A and 1B are block diagrams of a computing system 100, accordingto example embodiments. In some embodiments, the computing system 100 isassociated with (e.g., managed and/or operated by) a service provider,such as a business, an insurance provider, and the like. Referring firstto FIG. 1A, the computing system 100 includes a provider computingsystem 110, a telematics device 140, and a user device 150. Each of thecomputing systems of the computing system 100 are in communication witheach other and are connected by a network 105. Specifically, theprovider computing system 110, the telematics device 140, and the userdevice 150 are communicatively coupled to the network 105 such that thenetwork 105 permits the direct or indirect exchange of data, values,instructions, messages, and the like (represented by the double-headedarrows in FIG. 1A). In some embodiments, the network 105 is configuredto communicatively couple to additional computing system(s). Forexample, the network 105 may facilitate communication of data betweenthe provider computing system 110 and other computing systems associatedwith the service provider or with a customer of the service provider,such as a user device (e.g., a mobile device, smartphone, desktopcomputer, laptop computer, tablet, or any other computing system). Thenetwork 105 may include one or more of a cellular network, the Internet,Wi-Fi, Wi-Max, a proprietary provider network, a proprietary retail orservice provider network, and/or any other kind of wireless or wirednetwork.

In some embodiments, the provider computing system 110 may be a localcomputing system at a business location (e.g., a physical locationassociated with the service provider). In some embodiments, the providercomputing system 110 may be a remote computing system, such as a remoteserver, a cloud computing system, and the like. In some embodiments, theprovider computing system may be part of a larger computing system, suchas a multi-purpose server or other multi-purpose computing system. Insome embodiments, the provider computing system 110 may be implementedon a third-party computing device operated by a third-party serviceprovider (e.g., AWS, Azure, GCP, and/or other third party computingservices).

As shown in FIG. 1 , the provider computing system 110 includes aprocessing circuit 112, input/output (I/O) circuit 120, one or morespecialized processing circuits shown as an item damage severityaggregation circuit 124 and item damage severity modeling circuit 126,and a database 130. The processing circuit 112 may be coupled to the I/Ocircuit 120, the specialized processing circuits, and/or the database130. The processing circuit 112 may include a processor 114 and a memory116. The memory 116 may be one or more devices (e.g., RAM, ROM, Flashmemory, hard disk storage) for storing data and/or computer code forcompleting and/or facilitating the various processes described herein.The memory 116 may be or include non-transient volatile memory,non-volatile memory, and non-transitory computer storage media. Thememory 116 may include database components, object code components,script components, or any other type of information structure forsupporting the various activities and information structures describedherein. The memory 116 may be communicatively coupled to the processor114 and include computer code or instructions for executing one or moreprocesses described herein. The processor 114 may be implemented as oneor more application specific integrated circuits (ASICs), fieldprogrammable gate arrays (FPGAs), a group of processing components, orother suitable electronic processing components. As such, the providercomputing system 110 is configured to run a variety of applicationprograms and store associated data in a database of the memory 116(e.g., database 130).

The I/O circuit 120 is structured to receive communications from andprovide communications to other computing devices, users, and the likeassociated with the provider computing system 110. The I/O circuit 120is structured to exchange data, communications, instructions, and thelike with an I/O device of the components of the system 100. In someembodiments, the I/O circuit 120 includes communication circuitry forfacilitating the exchange of data, values, messages, and the likebetween the I/O device 120 and the components of the provider computingsystem 110. In some embodiments, the I/O circuit 120 includesmachine-readable media for facilitating the exchange of informationbetween the I/O circuit 120 and the components of the provider computingsystem 110. In some embodiments, the I/O circuit 120 includes anycombination of hardware components, communication circuitry, andmachine-readable media.

In some embodiments, the I/O circuit 120 may include a communicationinterface 122. The communication interface 122 may establish connectionswith other computing devices by way of the network 105. Thecommunication interface 122 may include program logic that facilitatesconnection of the provider computing system 110 to the network 105. Insome embodiments, the communication interface 122 may include anycombination of a wireless network transceiver (e.g., a cellular modem, aBluetooth transceiver, a Wi-Fi transceiver) and/or a wired networktransceiver (e.g., an Ethernet transceiver). For example, the I/Ocircuit 120 may include an Ethernet device, such as an Ethernet card andmachine-readable media, such as an Ethernet driver configured tofacilitate connections with the network 105. In some embodiments, thecommunication interface 122 includes the hardware and machine-readablemedia sufficient to support communication over multiple channels of datacommunication. Further, in some embodiments, the communication interface122 includes cryptography capabilities to establish a secure orrelatively secure communication session in which data communicated overthe session is encrypted.

In some embodiments, the I/O circuit 120 includes suitable I/O portsand/or uses an interconnect bus (e.g., bus 502 in FIG. 5 ) forinterconnection with a local display (e.g., a liquid crystal display, atouchscreen display) and/or keyboard/mouse devices (when applicable), orthe like, serving as a local user interface for programming and/or dataentry, retrieval, or other user interaction purposes. As such, the I/Ocircuit 120 may provide an interface for the user to interact withvarious applications and/or executables stored on the provider computingsystem 110. For example, the I/O circuit 120 may include a keyboard, akeypad, a mouse, joystick, a touch screen, a microphone, a biometricdevice, a virtual reality headset, smart glasses, and the like. Asanother example, I/O circuit 120, may include, but is not limited to, atelevision monitor, a computer monitor, a printer, a facsimile, aspeaker, and so on.

The memory 116 may store a database 130, according to some embodiments.The database 130 may retrievably store data associated with the providercomputing system 110 and/or any other component of the computing system100. That is, the data may include information associated with each ofthe components of the computing system 100. For example, the data mayinclude information about and/or received from the telematics device 140and/or the user device 150. The data may be retrievable, viewable,and/or editable by the provider computing system 110 (e.g., by userinput via the I/O circuit 120).

The database 130 may be configured to store one or more applicationsand/or executables to facilitate any of the operations described herein.In some arrangements, the applications and/or executables may beincorporated with an existing application in use by the providercomputing system 110. In some arrangements, the applications and/orexecutables are separate software applications implemented on theprovider computing system 110. The applications and/or executables maybe downloaded by the provider computing system 110 prior to its usage,hard coded into the memory 116 of the processing circuit 112, or be anetwork-based or web-based interface application such that the providercomputing system 110 may provide a web browser to access theapplication, which may be executed remotely from the provider computingsystem 110 (e.g., by a user device). Accordingly, the provider computingsystem 110 may include software and/or hardware capable of implementinga network-based or web-based application. For example, in someinstances, the applications and/or executables include componentswritten in HTML, XML, WML, SGML, PHP, CGI, and like languages. In thelatter instance, a user (e.g., a provider employee) may log onto oraccess the web-based interface before usage of the applications and/orexecutables. In this regard, the applications and/or executables may besupported by a separate computing system including one or more servers,processors, network interfaces, and so on, that transmit applicationsfor use to the provider computing system 110.

In the embodiment shown in FIG. 1A, the database 130 includes an itemdamage severity database 132 and a claims database 134.

The item damage severity database 132 is structured to store severityinformation, including actual severity information and/or predictedseverity information. In some embodiments, the severity information mayinclude item damage severity information. In some embodiments, theseverity information may include metadata associated with a claim, aclaim variable, a time period, a date, and/or other parameters relatedto the determined severity of item damage.

Item damage information can be received by parsing data from a claimsdata file or interface message and/or by parsing telematics data from adata file and/or interface message. According to an embodiment, theclaims database 134 is structured to store claims information for aplurality of claims. The claims information includes a plurality ofclaim variables for each claim. As used herein, the term “claimvariables” can include any data point that impacts the determination ofitem damage severity. The claim variables include but are not limitedto: an indication of whether the claim involved tow removal, a coveragecost, an indication of whether a vehicle door or doors is/are openableafter the accident, an indication of a fluid leak, an indication of aninsured car body type, an indication of whether the claim was alsoreported to authorities, a damage score, a report year, an indication ofprior damage to an insured item and/or an item associated with theinsured item, a loss to report lag time, a location (e.g., country,state, region, county, city, etc.), an indication of natural disasters,emergencies, disease outbreaks, or other parameters associated with thelocation, a state highway study, a time (e.g., year, month, week, day,date, hour, etc.), an indication of whether the claim is from a no-faultstate, state texting restrictions (e.g., phone usage restrictions),gross damage, an indication of whether a person was injured, anindication of liability, a claimant car cost, a FNOL report method, anexpected severity (described in detail herein below), and/or otherparameters related to the claim. In the embodiment shown in FIG. 1B, theclaims database 134 may be stored by electronic storage other than thedatabase 130. Accordingly, the computing system 100 may include aseparate claims database 172 that is stored at a claims processingserver 160.

According to various embodiments, the provider computing system 110includes any combination of hardware and software structured tofacilitate operations of the components of the computing system 100. Forexample, and as shown in FIG. 1 , the provider computing system includesan item damage severity aggregation circuit 124 and an item damageseverity modeling circuit 126 for determining percent severity impactfor each of a plurality of claim variables. More generally, the providercomputing system 110 may include any combination of hardware andsoftware including specialized processing circuits, applications,executables, and the like for controlling, managing, or facilitating theoperation of the other computing systems of the computing system 100including the telematics device 140 and/or the user device 150. Forexample, the provider computing system 110 may include a telematicsdevice interface circuit structured to receive and retrievably storedata from a remote telematics device, such as a telematics devicepositioned on-board of an insured item.

In some embodiments, the item damage severity aggregation circuit 124 isstructured to receive severity information. The severity information maybe received from the user device 150, the item damage severity database132, and/or another computing device communicatively coupled to thenetwork 105. The severity information may include actual severity datarelated to one or more claims. For example, the severity information mayinclude an actual severity value for a claim. As utilized herein,“actual severity data” refers to severity data that is fully known for aclaim or set of claims. For example, severity data may not be fullyknown for a claim or set of claims until after the time period in whicha loss occurred (e.g., when further item inspection, whether on-site orremote, is needed, when a further investigation related to thecircumstances surrounding an accident is needed, or under similarcircumstances which may affect the final payout amount and thecorresponding item damage severity determination). Accordingly, “actualseverity data” is severity data that is fully known when used by thesystems and methods described herein, when reported, etc.

As briefly described above, the severity may be measured by aquantitative value, such as a severity score or a dollar amount. In anexample embodiment, the severity data includes a quantitative value ofseverity for each claim of a plurality of claims. Additionally and/oralternatively the severity data includes a quantitative value for eachclaim variable of a plurality of claims. The item damage severityaggregation circuit 124 is structured to aggregate severity data andprovide actual severity data to other components of the providercomputing system 110, such as the item damage severity modeling circuit126 and/or the item damage severity database 132. In some embodiments,the item damage severity aggregation circuit 124 is also structured toprovide the claims data associated with the actual severity data toother components of the provider computing system 110.

The item damage severity modeling circuit 126 is structured to storecomputer-executable instructions embodying one or more machine learningmodels. The one or more machine learning models are configured togenerate one or more statistical models of damage severity. The itemdamage severity modeling circuit 126 may be structured to train the oneor more machine learning models based on the claims information and theseverity information such that the one or more machine learning modelsoutputs and/or determines a predicted severity. As used herein“predicted severity” is severity that is estimated or predicted, usingone or more statistical methods, machine learning algorithms, and thelike, by estimating the factors that are not fully known when damage isreported. For example, the one or more machine learning models may betrained using training data that includes claims data (e.g., stored atthe claims database 134 or at the claims database 172) and actualseverity data stored at the severity database 132. In some embodiments,the actual severity data may be provided by the item damage severityaggregation circuit 124. The one or more machine learning models aretrained to generate predicted severity based on the training data. Insome embodiments, the one or more machine learning models generatedecision trees to output and/or determine predicted severity based oninput claim data. For example, the item damage severity modeling circuit126 may receive claim data and identify (e.g., parse) one or more claimvariables from the received claim data. The item damage severitymodeling circuit 126 may utilize the one or more trained machinelearning models to determine and/or output the predicted severity. Asbriefly described above, the one or more machine learning models mayinclude a machine learning explanatory model (e.g., SHAP or anothersuitable model). Accordingly, the item damage severity modeling circuit126 may utilize the machine learning explanatory model with the one ormore machine learning models to output and/or determine a base rate ofexpected severity and/or explainer values. For example, the machinelearning explanatory model (e.g., SHAP or another suitable model) mayidentify the decisions made at the one or more decision trees andgenerate explainer values representing calculations performed at eachdecision juncture. The explainer values correspond to a claim variableof the claim data input into the one or more machine learning models.Specifically, the machine learning explanatory model generatesexplanatory values for each claim variable in the one or more decisiontrees. A sum of the explanatory values is equivalent to the output(e.g., the predicted severity). The base rate of severity is outputand/or determined by the machine learning explanatory model bycalculating an average actual severity of the training dataset.

In an example operational scenario, the item damage severity modelingcircuit 126 receives claims information (e.g., from the claims database134 or the claims database 172). The item damage severity modelingcircuit 126 may run code and/or executables that define the one or moremachine learning models. The code and/or executables may use parametersparsed from the claims data (e.g., claim variables) as inputs for themachine learning models. The code and/or executables may be embodied inthe item damage severity modeling circuit 126, stored by the memory 116,stored by the database 130, and/or accessed from a remote computingdevice via the network 105 and/or the communication interface 122. Thecode and/or executables may be compiled at runtime or before execution(e.g., an .exe file). Accordingly, the item damage severity modelingcircuit 126 may output and/or determine, using the one or more machinelearning models including the machine learning explanatory model, afirst set of explainer values for a first set of claims within a firsttime period (e.g., a target time period). The “explainer value” is aquantitative value associated with an input of the one or more machinelearning models. As described above, the one or more machine learningmodels receive claims data including claim variables for each claim asinput. The one or more machine learning models generates a predictedseverity for each claim. Accordingly, the “explainer value” is a valueassociated with each claim variable for each claim that is equivalent tothe partial predicted severity for each claim variable. The sum of allthe explainer values for a claim is equal to the predicted severity forthe claim. The explainer value may be positive (e.g., when the claimvariable is predicted to increase the total severity), negative (e.g.,when the claim variable is predicted to decrease the total severity), orzero (e.g., when the claim variable is predicted to have no impact onthe total severity). The item damage severity modeling circuit 126 mayoutput and/or determine, using the one or more machine learning modelsincluding the machine learning explanatory model, a second set ofexplainer values for a second set of claims within a second time period,where the first time period is after the second time period. The itemdamage severity modeling circuit 126 outputs and/or determines a totalof explainer values for each claim variable. The item damage severitymodeling circuit 126 then averages the explainer values for each claimvariable. The item damage severity modeling circuit 126 then calculatesa percent change in severity impact for each claim variable based on theaverage explainer value of the first time period, the average explainervalue of the second time period, and an actual severity for the claimsin the second time period. The item damage severity modeling circuit 126may be structured to output all determined values, including the percentchange in severity impact for each claim variable, as an output severitydata packet. The item damage severity modeling circuit 126 may also bestructured to generate a user interface that includes one or moregraphical features depicting the output severity data packet.

As shown, the telematics device 140 includes a processing circuit 142, asensor circuit 144 and an I/O circuit 146. The processing circuit 142and the I/O circuit 146 may be substantially similar in structure and/orfunction as the processing circuit 112 and I/O circuit 120. For example,the processing circuit 142 may include a processor and memory similar tothe processor 114 and memory 116, and the I/O circuit 120 may include acommunication interface 148 that is similar to the communicationinterface 122. Accordingly, the telematics device 140 maycommunicatively couple to the network 105 via the communicationinterface 148. The telematics device 140 is structured to sendtelematics data to other computing devices via the network 105. Thetelematics data may be detected by telematics device 140. In someembodiments, the telematics device 140 may transmit the telematics datato the provider computing system 110. For example, the telematics device140 may transmit the telematics data to the claims database 134 and/orto the item damage severity modeling circuit 126. The item damageseverity modeling circuit 126 may be structured to automaticallyre-train the one or more machine learning models using the telematicsdata and/or to automatically output and/or determine a predictedseverity based on the telematics data including one or more claims. Insome embodiments, the telematics device 140 transmits the telematicsdata to the claims processing server 160 (FIG. 1B).

The sensor circuit 144 may include any combination of hardware and/orsoftware for sensing telematics data. The hardware may include one ormore sensors, such as an accelerometer, a positioning sensor (e.g.,GPS), a vehicle interface sensor for interfacing with a computing systemof a vehicle (e.g., an ECM), a motion sensor, and the like. In someembodiments, the sensor circuit 144 may communicatively couple to one ormore external sensors via the I/O circuit 146. The software may includeappropriate programs, executables, drivers, etc. for operating the oneor more sensors and/or one or more external sensors. Accordingly, thetelematics data may include data detected by the one or more sensorssuch as acceleration data, braking data, an indication of an impact,and/or other data detected by the one or more sensors. The telematicsdata may further include data for any of the claim variables describedherein above. For example, the telematics data may include an indicationof an accident, an indication of whether a door is open, an indicationof acceleration before an accident, an indication of whether a vehiclewas towed from an accident, etc. In some embodiments, the telematicsdevice 140 may receive data from the user device 150, and the telematicsdata may include the data received from the user device 150. The datareceived from the user device 150 may include sensor data from a userdevice sensor, user data input by a user before or after an accident,and/or other data from the user device 150 associated with a claim.

The user device 150 includes a processing circuit 152 and an I/O circuit156. The processing circuit 152 and the I/O circuit 156 may besubstantially similar in structure and/or function as the processingcircuit 112 and I/O circuit 120. For example, the processing circuit 152may include a processor and memory similar to the processor 114 andmemory 116, and the I/O circuit 120 may include a communicationinterface 158 that is similar to the communication interface 122.Accordingly, the user device 150 may communicatively couple to thenetwork 105 via the communication interface 158. The user device 150 isstructured to send and receive data to/from other computing devices viathe network 105. The data may include claims data and/or severity data.For example, the user device 150 may be structured to collect claimsdata including values for one or more of the claim variables describedabove. The user device 150 may detect, by one or more user devicesensors, the claims data and/or the claims data may be entered into theuser device 150 by a user (e.g., a provider customer, a provideremployee, a provider agent, etc.). The user device 150 may also receivethe output severity data packet. The user device 150 may be configuredto display a user interface depicting aspects of the output severitydata packet. In some embodiments, the user interface is generated by theprovider computing system 110 (e.g., the item damage severity modelingcircuit 126), and displayed by the user device 150. In otherembodiments, the user interface is generated and displayed by the userdevice 150 based on the output severity data packet.

Now referring to FIG. 1B, the computing system 100 is shown to furtherinclude a claims processing server 160. The claims processing server 160includes a processing circuit 162, an I/O circuit 166, and a database170. The processing circuit 162 and the I/O circuit 166 may besubstantially similar in structure and/or function as the processingcircuit 112 and I/O circuit 120. For example, the processing circuit 162may include a processor and memory similar to the processor 114 andmemory 116, and the I/O circuit 166 may include a communicationinterface 168 that is similar to the communication interface 122.Accordingly, the claims processing server 160 may communicatively coupleto the network 105 via the communication interface 168. The database 170may be substantially similar to the database 130. The database 170 maystore a claims database 172 in addition to and/or alternatively to theclaims database 134.

In the embodiment shown in FIG. 1B, the telematics device 140 and/or theuser device 150 provide claims data to the claims processing server 160.The claims processing server 160 may store claims data including valuesfor each claim variable of every claim. The claims processing server mayprovide the claims data to the provider computing system 110.

In some embodiments, the provider computing system 110 and the claimsprocessing server 160 are the same computing device or devices such thatthe claims processing and item damage severity analysis are completed bythe same device. In other embodiments, the provider computing system 110and the claims processing server 160 are physically separate computingsystems that are communicatively coupled by the network 105.

FIG. 2 is a flow diagram including computer-based operations fortraining a machine learning model. In some arrangements, one or more ofthe computing systems of the system 100 may be configured to perform amethod 200. For example, the provider computing system 110 may bestructured to perform the method 200, alone or in combination with otherdevices, such as the telematics device 140, the user device 150, and/orthe claims processing server 160. In some embodiments, the method 200may include user inputs from a user (e.g., a provider employee), one ormore user devices (such as devices of provider employees), anothercomputing device on the network 105, and the like.

In broad overview of the method 200, at step 202, the provider computingsystem 110 provides claims data to the machine learning model. At step204, the provider computing system 110 provides actual item damageseverity data to the machine learning models. At step 206, providercomputing system 110 trains the machine learning model based on theclaims data and the actual item damage severity data. At step 208, theprovider computing system 110 generates an expected item damage severityoutput for given time intervals. At step 210, the provider computingsystem 110 queries a database for new data. At step 212 the machinelearning model is re-trained based on the new data, and the method 200repeats back to step 202 and/or step 204. In some arrangements, themethod 200 may include more or fewer steps than as shown in FIG. 2 .

Referring to the method 200 in more detail, at step 202, the providercomputing system 110 provides claims data to the machine learning model.For example, the item damage severity modeling circuit 126 may receivethe claims data from the claims database 134 and/or the claims database172. The item damage severity modeling circuit 126 may also receiveclaims data directly from the telematics device 140 and/or the userdevice 150. At step 204, the provider computing system 110 providesactual item damage severity data to the machine learning models. Forexample, the item damage severity modeling circuit 126 may receive theactual item damage severity data from the item damage severity database132.

At step 206, provider computing system 110 trains the machine learningmodel based on the claims data and the actual item damage severity data.The item damage severity modeling circuit 126 trains the machinelearning model(s) to predict item damage severity based on an inputincluding claims data. The claims data input may include values for oneor more claim variables for each claim. The one or more machine learningmodels may be trained using claims data from within a given time period(e.g., one day, one week, one month, etc.). In an example embodiment,the one or more machine learning models are trained using claims dataand actual severity data from a first time period. In an additionalexample embodiment, the one or more machine learning models are trainedusing a plurality of different configurations of the claims variablesand a plurality of combinations of model parameters to output and/ordetermine which of the one or more machine learning models generatesoutputs with higher accuracy. The one or more machine learning modelsoutput and/or determine estimated severity data from the claims data andare trained to target the actual severity data. The one or more machinelearning models may iteratively generate predicted severity data andself-correct until the predicted severity data is within a tolerancethreshold of the actual severity data. The tolerance threshold may be apredetermined threshold (e.g., within 10%, within 5%, etc.). The one ormore machine learning models may be re-trained or self-corrected ondemand (e.g., by user input) and/or automatically in real-time (e.g.,every second, every millisecond, every minute, etc.) and/or at regularintervals (e.g., every day, every week, every month, etc.).

At step 208, the provider computing system 110 generates an expecteditem damage severity output for given time intervals. The item damageseverity modeling circuit 126 may generate, based on the trained machinelearning models, an expected item damage severity output. The expecteditem damage severity output may be generated for a set of claims withina time period that does not have actual severity data.

At step 210, the provider computing system 110 queries a database fornew data. The item damage severity modeling circuit 126 may query thedatabase 130 and/or the database 170 for new claims data and/or newactual severity data. Advantageously, the process of retraining themachine learning model can be made fully automatic such that the itemdamage severity modeling circuit self-corrects as new actual severitydata becomes available in order to improve the accuracy of futurepredictions. Accordingly, the query that obtains new claims data and/ornew actual severity data may be automatically repeated in substantiallyreal-time (e.g., every minute, every 5 minutes, every hour, etc.) orperiodically (e.g., every day, every week, every month, etc.). At step212 the machine learning model is re-trained based on the new claimsdata and/or new actual severity data, and the method 200 repeats back tostep 202 and/or step 204. For example, when the item damage severitymodeling circuit 126 receives new actual severity for a set of claimswithin a time period, the item damage severity modeling circuit 126 mayre-train the one or more machine learning models based on the claimsdata and item damage severity data within the time period. The new datacan be run, by the item damage severity modeling circuit 126, throughthe steps of the method 200 to re-train the machine learning model.

FIG. 3 is a flow diagram including computer-based operations fordetermining a multi-variable percent change in item damage severity. Insome arrangements, one or more of the computing systems of the computingsystem 100 may be configured to perform the method 300. For example, theprovider computing system 110 may be structured to perform the method300, alone or in combination with other devices, such as the telematicsdevice 140, the user device 150, and/or the claims processing server160. In some embodiments, the method 300 may include user inputs from auser (e.g., a provider employee), one or more user devices (such asdevices of provider employees), another computing device on the network105, and the like.

In broad overview of method 300, at step 302, the provider computingsystem 110 generates an explainer value for each input variable of eachclaim received in a predetermined time period. At step 304, the providercomputing system 110 aggregates the explainer values for each claim. Atstep 306, the provider computing system 110 averages the aggregatedexplainer values based on a frequency of each claim. At step 308, theprovider computing system 110 calculates a percent impact due to eachvariable. In some arrangements, the method 300 may include more or fewersteps than as shown in FIG. 3 .

Referring to the method 300 in more detail, at step 302, the providercomputing system 110 generates an explainer value for each inputvariable of each claim received in a predetermined time period. Theexplainer values may be generated based on a relative impact each claimvariable has on the total item damage severity. The explainer values maybe generated using a machine learning explanatory model, such as SHAP.For example, the one or more machine learning models may generate one ormore decision trees to arrive at an output. The provider computingsystem 110 and/or one or more components thereof may utilize the machinelearning explanatory model with the one or more machine learning models.The machine learning explanatory model may identify the decisions madeat the one or more decision trees and generate explanatory valuesrepresenting calculations performed at each decision juncture. Theexplanatory values correspond to the claim variables of the claim datainput into the one or more machine learning models. In the embodimentsdescribed herein, the one or more machine learning models generatedecision trees to output and/or determine a predicted severity based onone or more claim variable inputs. The machine learning explanatorymodel generates explanatory values for each claim variable in the one ormore decision trees, and a sum of the explanatory values is equivalentto the output (e.g., the predicted severity). At step 304, the providercomputing system 110 aggregates the explainer values for each claim. Theexplainer values may be aggregated for a set of claims. For example, theexplainer value for one type of claim variable is added up resulting ina total item damage severity for a single claim variable across the setof claims. In some embodiments, the provider computing system 110 sumsexplainer values for one or more line IDs to the claim level. Forexample, a single claim may have one or more line IDs and/or the claimmay include more than one insured item. Accordingly, one or more of theline IDs may be related to a first insured item, a second insured item,and so on. The provider computing system 110 may sum the explainervalues for each of the one or more line IDs of a claim. Accordingly,when the claim is a multi-coverage claim (e.g., a claim having more thanone line ID and/or a claim related to more than one insured item) theline IDs are aggregated such that each claim is associated with asingle, aggregated value. This process may be repeated for some or allof the claim variables. In various embodiments, the set of claims can beaggregated according to the FNOL date, loss date, insured item type,make and/or model, geographical location of loss (e.g., GPS coordinates,zip code, etc.), or any suitable combination thereof. In an exampleembodiment, the set of claims is aggregated according to a claimidentifier (e.g., a claim number, a claim ID, etc.).

At step 306, the provider computing system 110 averages the aggregatedexplainer values. The average is calculated by multiplying a relativefrequency (e.g., percent occurrence of each claim variable in the claimswithin the predetermined time period) of a claim variable by thecorresponding aggregated explainer value (e.g., for the same claimvariable). That is, an aggregated explainer value for a first claimvariable (X₁) is multiplied by the percent occurrence of that claimvariable (Y₁) within the predetermined time period (e.g., within a week,a month, a quarter, a year, etc.). For example, a first claim variablemay be a type of vehicle where X₁ is an aggregated explainer value forthe vehicle type and where Y₁ is a percentage of claims that include thevehicle type. The average explainer value is calculated as the productof X₁ and Y₁. In some embodiments, the average explainer value may becalculated for each claim variable of a plurality of claims within thepredetermined time period. In some embodiments, the average explainervalue may be calculated for at least one claim variable for theplurality of claims within the predetermined time period.

At step 308, the provider computing system 110 calculates a percentimpact due to each variable. The percent impact due to each claimvariable is calculated as a percent change in average explainer valuefor each claim variable between a first time period and a second timeperiod. The first time period may be a target time period. The secondtime period may be a time period before the first time period (e.g., onemonth before, one year before, etc.). First, the item damage severitymodeling circuit 126 sums the average explainer values for the firsttime period and sums the average explainer values for the second timeperiod. The result is a predicted item damage severity for the firsttime period (S1) and a predicted item damage severity for the secondtime period (S2). That is, according to an embodiment, the predicteditem damage severity for the first time period (S1) is equal to the sumof each explainer value (X_(i1)) for each claim in the first time periodmultiplied by the percent occurrence of that claim variable (Y_(i1))within the first time period. Similarly, the predicted item damageseverity for the second time period (S2) is equal to the sum of eachexplainer value (X_(i2)) for each claim in the second time periodmultiplied by the percent occurrence of that claim variable (Y_(i2))within the second time period.

The percent impact due to each claim variable is calculated bysubtracting the average explainer value for a claim variable for thesecond time period (E2) from the average explainer value for that sameclaim variable for the first time period (E1) and dividing the result bythe predicted item damage severity for the second time period (S2). Theresult is a percent impact due to a single claim variable based on thepredicted severity (I1). Accordingly, this process may be repeated foreach claim variable of the claims in the first time period. The itemdamage severity modeling circuit 126 may correct the percent impact dueto each claim variable to be based on the actual severity of the secondtime period (I2). An example equation (1) is shown below.

I1=(E1−E2)/S2  (1)

The provider computing system 110 may correct the percent impactbecause, in some embodiments, there may be a difference between thepredicted severity in a time period (e.g., the first time period) andthe actual severity in the same time period. The difference may be dueto inaccuracies of the machine learning model(s) (including machinelearning explanatory model(s)) used to generate the explainer values,predicted severity, etc. To correct the results, the item damageseverity modeling circuit 126 calculates a percent change between thepredicted severity of the first time period (S1) and the predictedseverity of the second time period (S2) resulting in a predictedseverity percent change (P1). The item damage severity modeling circuit126 then calculates a percent change between the actual severity of thesecond time period (A2) and the predicted severity of the first timeperiod (S1) resulting in an actual severity percent change (P2). Theitem damage severity modeling circuit 126 calculates the percent impactdue to each claim variable based on the actual severity of the secondtime period (I2) by multiplying the percent impact due to each claimvariable based on the predicted severity (I1) by the actual severitypercent change (P2) and dividing by the predicted severity percentchange (P1). The result is a percent impact due to each claim variablebased on the actual severity of the second time period (I2), and may beoutput as the output severity data packet. An example equation (2) isshown below.

I2=I1(P2/P1)  (2)

FIG. 4A is an illustration showing various aspects of a user interface400, according to an example embodiment. FIGS. 4B-4D are illustrationsshowing various aspects of the user interface 400 of FIG. 4A. As brieflydescribed above, the user interface 400 may be generated and displayedby one or more of the computing systems of the system 100. For example,the user interface 400 may be generated by the provider computing system110 and/or the user device 150. The user interface 400 may be displayedby a display of the provider computing system 110 and/or a display ofthe user device 150.

The user interface 400 includes one or more graphical representations ofthe data described herein above, such as the output severity datapacket, the claims data, the severity data, etc. The first graphicalfeature 410 may include a graph comparing actual severity and predictedseverity of a predetermined time period (e.g., the first time period).The predicted severity is calculated (e.g., by the provider computingsystem 110 and/or one or more components thereof) by summing thepredicted severities for each line ID to the claim level (to account forclaims having more than one line ID and/or more than one insured item)and then averaging the sum over a predetermined time period (e.g., thefirst time period).

The third graphical feature 430 may include a percent change in severitybetween the first time period and the second time period for a series oftime periods. For example, the third graphical feature may include apercent change in severity between months of two years, such as apercent change between January of a first year and January of a secondyear. The graph may show multiple months in succession to show thechange in percent change in severity over time. As shown, the thirdgraphical feature 430 may include an actual percent change in severitybetween the first time period and the second time period (shown by theline graph) and a predicted percent change in severity between the firsttime period and the second time period (shown by the bar graph).Accordingly, the third graphical feature 430 visually represents adifference between the actual percent change in severity between thefirst time period and the second time period and the predicted percentchange in severity between the first time period and the second timeperiod.

The second graphical feature 420 may include a waterfall graph showingthe percent impact due to each claim variable for each claim variable.Each percent impact is a part of the total change in severity betweentwo predetermined time periods (e.g., the percent change from the firsttime period compared to the second time period). The second graphicalfeature may include one or more graphical features 422 representing eachclaim variable. The one or more graphical features 422 may be colorcoded to denote a positive or negative value. The one or more graphicalfeatures 422 may be ordered by value (e.g., from largest to smallest).The one or more graphical features 422 may be filtered automatically, bythe provider computing system 110 (e.g., by the item damage severitymodeling circuit 126), such that only percent impacts that are greaterin absolute value compared to a threshold value are displayed. In someembodiments and as shown in FIG. 4B, the second graphical feature 420may include an “other” graphical feature that aggregates all the percentchange in severity values that were filtered out into a separatecategory that is displayed separately from the other values. In someembodiments and as shown in FIG. 4B, the second graphical feature mayinclude a “total” graphical feature that aggregates all the percentimpacts for each claim variable representing a total percent impactchange.

The fourth graphical feature 440 includes a detailed list of claimsdata, item damage severity data, and/or percent impact data of the claimvariables of the second graphical feature 420. For example, the fourthgraphical feature 440 may display, by default, a list of claim variablesand the percent impact for each claim variable, as shown in FIG. 4C. Thelist of claim variables may be filtered automatically, by the providercomputing system 110 (e.g., by the item damage severity modeling circuit126), such that only percent impacts that are greater in absolute valuecompared to a threshold value are displayed. Claim variables havingpercent impacts that are less than the threshold value are grouped intoan “other” category. The threshold may be a predetermined threshold thatmay be adjusted by a user (e.g., via a user input). The fourth graphicalfeature 440 may also include a “total” category that aggregates all thepercent impacts for each claim variable and displays a total percentimpact change. In an example embodiment, the fourth graphical feature440 displays a list of claim variables (including the “other” categoryand/or the “total” category) that is displayed in the second graphicalfeature 420. When one or more of the graphical features 422 is selectedby a user (e.g., by input through the I/O circuit 120), the fourthgraphical feature 440 may display a detailed graphical feature 442 thatincludes a list of values for the selected graphical feature 422, asshown in FIG. 4D. For example, if a user selects a first graphicalfeature 422, the fourth graphical feature 440 may display thecorresponding claim variable, percent change in severity value, anindication of whether the percent change in severity is positive ornegative (e.g., by a color or arrow), an average value for the claimvariable for the first time period, a change in average value for theclaim variable between the first time period and the second time period,and/or other values associated with the corresponding claim variable.

In an example embodiment, a user may select a first claim variable fromthe claim variables shown in the second graphical feature 420. As shownin FIG. 4D, the selected variable is “Coverage Count” (e.g., a totalnumber of coverages associated with a claim). The fourth graphicalfeature 440 may display a detailed graphical feature 442 that includesan average value of the claim variable (determined by averaging theclaim variable values for all claims in a predetermined time period). Insome embodiments, the claim variable values are numerical and theaverage is calculated and displayed. In other embodiments, the claimvariable values are qualitative (e.g., “yes”, “no”, “unknown”, etc.) andthe detailed graphical feature 442 includes a frequency percentageinstead of the average. For example, the user may select a second claimvariable, such as a “tow removal” variable. The detailed graphicalfeature 442 may display a frequency of occurrences of “tow removal”instead of an average value. For example, the detailed graphical feature442 may display a percent of claims in the predetermined time periodthat have each level of the categorical variable selected (e.g. the % ofvehicles in the predetermined time period that had “Yes” for “TowRemoval”).

The result is an improved user interface that advantageouslyautomatically sorts graphical features representing percent impact indescending order, filters the percent change in severity values based ona threshold such that only the largest in magnitude are shown (e.g.,such that a user can easily read the graph and determine the mostimpactful claim variables), and is selectable by a user to viewadditional data on the user interface.

FIG. 5 is a component diagram of an example computing system suitablefor use in the various embodiments described herein. For example, thecomputing system 500 may implement an example provider computing system110, the telematics device 140, the user device 150, and/or variousother example systems and devices described in the present disclosure.

The computing system 500 includes a bus 502 or other communicationcomponent for communicating information and a processor 504 coupled tothe bus 502 for processing information. The computing system 500 alsoincludes main memory 506, such as a random access memory (RAM) or otherdynamic storage device, coupled to the bus 502 for storing information,and instructions to be executed by the processor 504. Main memory 506can also be used for storing position information, temporary variables,or other intermediate information during execution of instructions bythe processor 504. The computing system 500 may further include a readonly memory (ROM) 508 or other static storage device coupled to the bus502 for storing static information and instructions for the processor504. A storage device 510, such as a solid state device, magnetic diskor optical disk, is coupled to the bus 502 for persistently storinginformation and instructions.

The computing system 500 may be coupled via the bus 502 to a display514, such as a liquid crystal display, or active matrix display, fordisplaying information to a user. An input device 512, such as akeyboard including alphanumeric and other keys, may be coupled to thebus 502 for communicating information, and command selections to theprocessor 504. In another embodiment, the input device 512 has a touchscreen display. The input device 512 can include any type of biometricsensor, a cursor control, such as a mouse, a trackball, or cursordirection keys, for communicating direction information and commandselections to the processor 504 and for controlling cursor movement onthe display 514.

In some embodiments, the computing system 500 may include acommunications adapter 516, such as a networking adapter. Communicationsadapter 516 may be coupled to bus 502 and may be configured to enablecommunications with a computing or communications network 105 and/orother computing systems. In various illustrative embodiments, any typeof networking configuration may be achieved using communications adapter516, such as wired (e.g., via Ethernet), wireless (e.g., via Wi-Fi,Bluetooth), satellite (e.g., via GPS) pre-configured, ad-hoc, LAN, WAN,and the like.

According to various embodiments, the processes that effectuateillustrative embodiments that are described herein can be achieved bythe computing system 500 in response to the processor 504 executing anarrangement of instructions contained in main memory 506. Suchinstructions can be read into main memory 506 from anothercomputer-readable medium, such as the storage device 510. Execution ofthe arrangement of instructions contained in main memory 506 causes thecomputing system 500 to perform the illustrative processes describedherein. One or more processors in a multi-processing arrangement mayalso be employed to execute the instructions contained in main memory506. In alternative embodiments, hard-wired circuitry may be used inplace of or in combination with software instructions to implementillustrative embodiments. Thus, embodiments are not limited to anyspecific combination of hardware circuitry and software.

The embodiments described herein have been described with reference todrawings. The drawings illustrate certain details of specificembodiments that implement the systems, methods and programs describedherein. However, describing the embodiments with drawings should not beconstrued as imposing on the disclosure any limitations that may bepresent in the drawings.

It should be understood that no claim element herein is to be construedunder the provisions of 35 U.S.C. § 112(f), unless the element isexpressly recited using the phrase “means for.”

As used herein, the term “circuit” (e.g., “engine”) may include hardwarestructured to execute the functions described herein. In someembodiments, each respective “circuit” may include machine-readablemedia for configuring the hardware to execute the functions describedherein. The circuit may be embodied as one or more circuitry componentsincluding, but not limited to, processing circuitry, network interfaces,peripheral devices, input devices, output devices, sensors, etc. In someembodiments, a circuit may take the form of one or more analog circuits,electronic circuits (e.g., integrated circuits (IC), discrete circuits,system on a chip (SOCs) circuits, etc.), telecommunication circuits,hybrid circuits, and any other type of “circuit.” In this regard, the“circuit” may include any type of component for accomplishing orfacilitating achievement of the operations described herein. Forexample, a circuit as described herein may include one or moretransistors, logic gates (e.g., NAND, AND, NOR, OR, XOR, NOT, XNOR,etc.), resistors, multiplexers, registers, capacitors, inductors,diodes, wiring, and so on.

The “circuit” may also include one or more processors communicativelycoupled to one or more memory or memory devices. In this regard, the oneor more processors may execute instructions stored in the memory or mayexecute instructions otherwise accessible to the one or more processors.In some embodiments, the one or more processors may be embodied invarious ways. The one or more processors may be constructed in a mannersufficient to perform at least the operations described herein. In someembodiments, the one or more processors may be shared by multiplecircuits (e.g., circuit A and circuit B may comprise or otherwise sharethe same processor which, in some example embodiments, may executeinstructions stored, or otherwise accessed, via different areas ofmemory). Alternatively or additionally, the one or more processors maybe structured to perform or otherwise execute certain operationsindependent of one or more co-processors. In other example embodiments,two or more processors may be coupled via a bus to enable independent,parallel, pipelined, or multi-threaded instruction execution. Eachprocessor may be implemented as one or more general-purpose processors,application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), digital signal processors (DSPs), or other suitableelectronic data processing components structured to execute instructionsprovided by memory. The one or more processors may take the form of asingle core processor, multi-core processor (e.g., a dual coreprocessor, triple core processor, quad core processor, etc.),microprocessor, etc. In some embodiments, the one or more processors maybe external to the apparatus, for example the one or more processors maybe a remote processor (e.g., a cloud based processor). Alternatively oradditionally, the one or more processors may be internal and/or local tothe apparatus. In this regard, a given circuit or components thereof maybe disposed locally (e.g., as part of a local server, a local computingsystem, etc.) or remotely (e.g., as part of a remote server, such as acloud based server). To that end, a “circuit” as described herein mayinclude components that are distributed across one or more locations.

An example system for implementing the overall system or portions of theembodiments might include a general purpose computing computers in theform of computers, including a processing unit, a system memory, and asystem bus that couples various system components including the systemmemory to the processing unit. Each memory device may includenon-transient volatile storage media, non-volatile storage media,non-transitory storage media (e.g., one or more volatile and/ornon-volatile memories), etc. In some embodiments, the non-volatile mediamay take the form of ROM, flash memory (e.g., flash memory, such asNAND, 3D NAND, NOR, 3D NOR, etc.), EEPROM, MRAM, magnetic storage, harddiscs, optical discs, etc. In other embodiments, the volatile storagemedia may take the form of RAM, TRAM, ZRAM, etc. Combinations of theabove are also included within the scope of machine-readable media. Inthis regard, machine-executable instructions comprise, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions. Each respective memory devicemay be operable to maintain or otherwise store information relating tothe operations performed by one or more associated circuits, includingprocessor instructions and related data (e.g., database components,object code components, script components, etc.), in accordance with theexample embodiments described herein.

It should also be noted that the term “input devices,” as describedherein, may include any type of input device including, but not limitedto, a keyboard, a keypad, a mouse, joystick or other input devicesperforming a similar function. Comparatively, the term “output device,”as described herein, may include any type of output device including,but not limited to, a computer monitor, printer, facsimile machine, orother output devices performing a similar function.

It should be noted that although the diagrams herein may show a specificorder and composition of method steps, it is understood that the orderof these steps may differ from what is depicted. For example, two ormore steps may be performed concurrently or with partial concurrence.Also, some method steps that are performed as discrete steps may becombined, steps being performed as a combined step may be separated intodiscrete steps, the sequence of certain processes may be reversed orotherwise varied, and the nature or number of discrete processes may bealtered or varied. The order or sequence of any element or apparatus maybe varied or substituted according to alternative embodiments.Accordingly, all such modifications are intended to be included withinthe scope of the present disclosure as defined in the appended claims.Such variations will depend on the machine-readable media and hardwaresystems chosen and on designer choice. It is understood that all suchvariations are within the scope of the disclosure. Likewise, softwareand web implementations of the present disclosure could be accomplishedwith standard programming techniques with rule based logic and otherlogic to accomplish the various database searching steps, correlationsteps, comparison steps and decision steps.

The foregoing description of embodiments has been presented for purposesof illustration and description. It is not intended to be exhaustive orto limit the disclosure to the precise form disclosed, and modificationsand variations are possible in light of the above teachings or may beacquired from this disclosure. The embodiments were chosen and describedin order to explain the principles of the disclosure and its practicalapplication to enable one skilled in the art to utilize the variousembodiments and with various modifications as are suited to theparticular use contemplated. Other substitutions, modifications, changesand omissions may be made in the design, operating conditions andarrangement of the embodiments without departing from the scope of thepresent disclosure as expressed in the appended claims.

What is claimed is:
 1. A provider computing system comprising: acommunication interface structured to communicatively couple theprovider computing system to a network; a claims database storing claimsinformation for a plurality of claims, the claims information comprisinga plurality of claim variables; an item damage severity database storingseverity information; an item damage severity modeling circuit storingcomputer-executable instructions embodying one or more machine learningmodels; at least one processor; and memory storing instructions that,when executed by the at least one processor, cause the at least oneprocessor to: receive a first claim dataset corresponding to a firsttime period; parse a first plurality of variables from the first claimdataset; receive a second claim dataset corresponding to a second timeperiod before the first time period; parse a second plurality ofvariables from the second claim dataset; cause, by the item damageseverity modeling circuit, the one or more machine learning models toparse a first plurality of explainer values from the first claim datasetand a second plurality of explainer values from the second claimdataset; determine a first plurality of average explainer values foreach of the first plurality of explainer values and a second pluralityof average explainer values for each of the second plurality ofexplainer values; determine percent impact values, wherein each of thepercent impact values correspond to a first claim variable of the firstplurality of variables and a second claim variable of the secondplurality of variables, and wherein the first claim variable correspondsto the second claim variable; generate and render, via a display of acomputing device, a damage severity user interface comprising one ormore selectable features, the one or more selectable features eachrepresenting one of the percent impact values; and filter and sort theone or more selectable features based on the percent impact values and apredetermined impact threshold such that the one or more selectablefeatures representing the percent impact values that are above thepredetermined impact threshold are ordered in descending order.
 2. Theprovider computing system of claim 1, wherein the claims database isstructured to communicatively couple to a telematics device via thenetwork, wherein the telematics device is associated with an insureditem.
 3. The provider computing system of claim 2, wherein thetelematics device is structured to detect, by one or more sensors, oneor more impact parameter values associated with the insured item; andwherein the claims information comprises the one or more impactparameter values provided by the telematics device.
 4. The providercomputing system of claim 1, wherein the instructions further cause theat least one processor to train, by the item damage severity modelingcircuit, the one or more machine learning models based on a first subsetof the claims information and a first subset of the severity informationsuch that the one or more machine learning models outputs a predictedseverity based on an input claim dataset, wherein the first subset ofclaims information corresponds to a third time period.
 5. The providercomputing system of claim 4, wherein the third time period is at leastpartially before the second time period.
 6. The provider computingsystem of claim 4, wherein determining a first percent impact value ofthe percent impact values comprises: determining a difference between afirst explainer value and a second explainer value, wherein the firstexplainer value is associated with the first claim variable and thesecond explainer value is associated with the second claim variable; anddividing the difference by the predicted severity corresponding to thefirst claim variable within the second time period.
 7. The providercomputing system of claim 6, wherein the instructions further cause theat least one processor to: generate, by an item damage severityaggregation circuit of the provider computing system, a first actualseverity value for each of the claim variables within the second timeperiod; determine, by the item damage severity modeling circuit, a firstpercent change between the first plurality of average explainer valuesand the second plurality of average explainer values; determine, by theitem damage severity modeling circuit, a second percent change betweenthe first plurality of average explainer values and the first actualseverity value; and correct, by the item damage severity modelingcircuit, the first percent impact value by multiplying the first percentimpact value by the second percent change divided by the first percentchange.
 8. The provider computing system of claim 7, wherein theseverity user interface is structured to display, on the display andresponsive to a first selectable feature of the one or more selectablefeatures being selected, a detailed list of impact data associated withthe first percent impact value, wherein the first selectable feature isassociated with the first percent impact value.
 9. A method comprising:communicatively coupling, by a communication interface, a providercomputing system to a network; storing, by a claims database, claimsinformation for a plurality of claims, the claims information comprisinga plurality of claim variables; storing, by an item damage severitydatabase, severity information; storing, by an item damage severitymodeling circuit, computer-executable instructions embodying one or moremachine learning models; receiving a first claim dataset correspondingto a first time period; parsing a first plurality of variables from thefirst claim dataset; receiving a second claim dataset corresponding to asecond time period before the first time period; parsing a secondplurality of variables from the second claim dataset; causing, by anitem damage severity modeling circuit of the provider computing system,the one or more machine learning models to parse a first plurality ofexplainer values from the first claim dataset and a second plurality ofexplainer values from the second claim dataset; determining a firstplurality of average explainer values for each of the first plurality ofexplainer values and a second plurality of average explainer values foreach of the second plurality of explainer values; determining percentimpact values, wherein each of the percent impact values correspond to afirst claim variable of the first plurality of variables and a secondclaim variable of the second plurality of variables, and wherein thefirst claim variable corresponds to the second claim variable;generating and rendering, via a display of a computing device, a damageseverity user interface comprising one or more selectable features, theone or more selectable features each representing one of the percentimpact values; and filtering and sorting the one or more selectablefeatures based on the percent impact values and a predetermined impactthreshold such that the one or more selectable features representing thepercent impact values that are above the predetermined impact thresholdare ordered from left to right in descending order.
 10. The method ofclaim 9, further comprising: communicatively coupling, by thecommunication interface, the claims database to a telematics device viathe network, wherein the telematics device is associated with an insureditem; detecting, by one or more sensors of the telematics device, one ormore impact parameter values associated with the insured item; andreceiving, by the claims database and via the communication interface,the claims information, the claims information comprising the one ormore impact parameter values provided by the telematics device.
 11. Themethod of claim 9, further comprising training, by the item damageseverity modeling circuit, the one or more machine learning models basedon a first subset of the claims information and a first subset of theseverity information such that the one or more machine learning modelsoutputs a predicted severity based on an input claim dataset, whereinthe first subset of claims information corresponds to a third timeperiod.
 12. The method of claim 11, wherein the third time period is atleast partially before the second time period.
 13. The method of claim11, wherein determining a first percent impact value of the percentimpact values comprises: determining a difference between a firstexplainer value and a second explainer value, wherein the firstexplainer value is associated with the first claim variable and thesecond explainer value is associated with the second claim variable; anddividing the difference by the predicted severity corresponding to thefirst claim variable within the second time period.
 14. The providercomputing system of claim 13, wherein the instructions further cause theat least one processor to: generate, by an item damage severityaggregation circuit of the provider computing system, a first actualseverity value for each of the claim variables within the second timeperiod; determine, by the item damage severity modeling circuit, a firstpercent change between the first plurality of average explainer valuesand the second plurality of average explainer values; determine, by theitem damage severity modeling circuit, a second percent change betweenthe first plurality of average explainer values and the first actualseverity value; and correct, by the item damage severity modelingcircuit, the first percent impact value by multiplying the first percentimpact value by the second percent change divided by the first percentchange.
 15. The provider computing system of claim 15, wherein theseverity user interface is structured to display, on the display andresponsive to a first selectable feature of the one or more selectablefeatures being selected, a detailed list of impact data associated withthe first percent impact value, wherein the first selectable feature isassociated with the first percent impact value.
 16. Non-transitorycomputer readable media having computer executable instructions embodiedtherein that, when executed by at least one processor of a computingsystem, cause the computing system to perform operations for generatingmulti-variable severity values, the operations comprising:communicatively couple, by a communication interface, to a network;store, by a claims database, claims information for a plurality ofclaims, the claims information comprising a plurality of claimvariables; store, by an item damage severity database, severityinformation; store, by an item damage severity modeling circuit,computer-executable instructions embodying one or more machine learningmodels receive a first claim dataset corresponding to a first timeperiod; parse a first plurality of variables from the first claimdataset; receive a second claim dataset corresponding to a second timeperiod before the first time period; parse a second plurality ofvariables from the second claim dataset; cause the one or more machinelearning models to parse a first plurality of explainer values from thefirst claim dataset and a second plurality of explainer values from thesecond claim dataset; determine a first plurality of average explainervalues for each of the first plurality of explainer values and a secondplurality of average explainer values for each of the second pluralityof explainer values; determine percent impact values, wherein each ofthe percent impact values correspond to a first claim variable of thefirst plurality of variables and a second claim variable of the secondplurality of variables, and wherein the first claim variable correspondsto the second claim variable; generate and render, via a display of acomputing device, a damage severity user interface comprising one ormore selectable features, the one or more selectable features eachrepresenting one of the percent impact values; and filter and sort theone or more selectable features based on the percent impact values and apredetermined impact threshold such that the one or more selectablefeatures representing the percent impact values that are above thepredetermined impact threshold are ordered from left to right indescending order.
 17. The media of claim 16, wherein the operationsfurther comprise: communicatively couple, by the communicationinterface, the claims database to a telematics device via the network,wherein the telematics device is associated with an insured item;detect, by one or more sensors of the telematics device, one or moreimpact parameter values associated with the insured item; and receive,by the claims database and via the communication interface, the claimsinformation, the claims information comprising the one or more impactparameter values provided by the telematics device.
 18. The media ofclaim 16, wherein the operations further comprise: train, by the itemdamage severity modeling circuit, the one or more machine learningmodels based on a first subset of the claims information and a firstsubset of the severity information such that the one or more machinelearning models outputs a predicted severity based on an input claimdataset, wherein the first subset of claims information corresponds to athird time period, and wherein the third time period is at leastpartially before the second time period.
 19. The media of claim 18,wherein determining a first percent impact value of the percent impactvalues comprises: determining a difference between a first explainervalue and a second explainer value, wherein the first explainer value isassociated with the first claim variable and the second explainer valueis associated with the second claim variable; and dividing thedifference by the predicted severity corresponding to the first claimvariable within the second time period.
 20. The media of claim 19,wherein the operations further comprise: generate, by an item damageseverity aggregation circuit of the provider computing system, a firstactual severity value for each of the claim variables within the secondtime period; determine, by the item damage severity modeling circuit, afirst percent change between the first plurality of average explainervalues and the second plurality of average explainer values; determine,by the item damage severity modeling circuit, a second percent changebetween the first plurality of average explainer values and the firstactual severity value; and correct, by the item damage severity modelingcircuit, the first percent impact value by multiplying the first percentimpact value by the second percent change divided by the first percentchange.